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Skill Li-Evan/Bloom/skills/learn-graph

当用户要系统学一个新领域、不知道从哪入手、或担心「学得不够系统」时使用。用「知识图谱学习法」和用户一起构建该领域的概念/用途/父子节点图谱(自己建图的过程本身就是学习),标出复用价值最高的节点和「从常识就能入门的点」,给出有效学习路径并回答「学到哪算够」。触发场景:系统学 X 领域、从哪开始学、学得不系统、想要 X 的全貌、规划学习路径、这个领域有多大。From its SKILL.md

Install
npx -y skills add Li-Evan/Bloom --skill learn-graph

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

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知识图谱学习法(learn-graph)

核心信条:自己一步步建图谱的过程,本身就是最有效的学习——不要直接套用别人给的图谱。 绝大部分知识,都有一个从常识就能入门的点。

何时用

用户要系统进入一个新领域,或焦虑"学得不够系统 / 不知何时算够"。

流程(关键:和用户一起建,不是直接灌一张完整图)

第一步:锁定目标领域 X 和目的

用户为什么学 X?(接 learn-occam 的"既定问题")目的决定图谱画到多细。

第二步:构建图谱——只抓三件事

概念/名称 · 用途 · 上下文关系(父子节点)

  • 子节点 = X 依托 / 基于什么;父节点 = X 服务于什么目标。
  • 以提问引导用户一起填(自己建图才学得到),别一次性灌完。先给骨架,留节点让他补。

第三步:标注两个关键

  • 复用价值:哪些节点父节点多(像 Python)→ 优先学,回报最高。
  • 入门点:哪个节点"从常识就能入门"→ 学习路径的起点。

第四步:输出学习路径 + 颗粒度

从入门点出发、沿父子关系排一条有效路径。颗粒度按需自由切换(领域图 → 细分学科图)。"学到哪算够"= 覆盖到能解决第一步那个目的的节点即可,不必学满。

第五步:交接

  • 拿不准某节点是不是缺前置知识 → 这正是图谱的强项,已在图上标出。
  • 找到入门点要动手 → 转 learn-prototype(在图上找"最垃圾原型"的起点)。

注意

⚠️ 铁律·只用确证的已会知识:判断用户「已经会什么」只能用他确证学过的知识(亲口确认或可靠背景);严禁把「正在讲的材料 / 文章作者背景 / 对话里别人的知识」当成用户会的。拿不准 → 直接问「⚠️ 你学过 ___ 吗?」,绝不替他假设。

  • 强调"自己建":多用提问让用户参与,别炫一张完美的图。
  • 同族 skill:learn-occam(该不该学) learn-crossover(已会什么) learn-prototype(动手) learn-feynman(自查)。

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most learn study skills give in 821 tokens

Counted across 545 of the 593 authors here whose files we hold, read 2026-09-06

  • Treat the current directory as a teaching workspacein 20 of 545, across 17 files
  • Teach knowledge first then practice skillsin 19 of 545, across 16 files
  • Design lessons which build long-term retentionin 15 of 545, across 12 files
  • Save each lesson as a self-contained HTML filein 15 of 545, across 12 files
  • Question the user on why they want to learn thisin 15 of 545, across 12 files
  • Reuse components from the assets directoryin 14 of 545, across 11 files
  • Never trust your parametric knowledgein 13 of 545, across 10 files
  • Record user preferences in NOTES.mdin 11 of 545, across 8 files
  • Ground all teaching in the MISSION.md documentin 11 of 545, across 8 files
  • Save each lesson to the lessons directoryin 8 of 545
  • Question the user if the mission is unclearin 7 of 545
  • Gather primary sources onlyin 7 of 545, across 4 files

Said here and by no other author read

  • Lock target domain and purpose first
  • Build graph focusing on concept purpose and context
  • Mark reuse value and common sense entry point
  • Output learning path and granularity
  • Guide user to build graph together
  • Ask when unsure about user knowledge

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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